# arcgis.features.analyze_patterns module¶

calculate_density takes known quantities of some phenomenon and spreads these quantities across the map. find_hot_spots identifies statistically significant clustering in the spatial pattern of your data. interpolate_points predicts values at new locations based on measurements found in a collection of points.

## calculate_density¶

analyze_patterns.calculate_density(field=None, cell_size=None, cell_size_units='Meters', radius=None, radius_units=None, bounding_polygon_layer=None, area_units=None, classification_type='EqualInterval', num_classes=10, output_name=None, context=None, gis=None)

The calculate_density function creates a density map from point or line features by spreading known quantities of some phenomenon (represented as attributes of the points or lines) across the map. The result is a layer of areas classified from least dense to most dense.

input_layerRequired layer (see Feature Input in documentation)

The point or line features from which to calculate density.

fieldOptional string

A numeric field name specifying the number of incidents at each location. If not specified, each location will be assumed to represent a single count.

cell_sizeOptional float

This value is used to create a mesh of points where density values are calculated. The default is approximately 1/1000th of the smaller of the width and height of the analysis extent as defined in the context parameter.

cell_size_unitsOptional string

The units of the cellSize value

A distance specifying how far to search to find point or line features when calculating density values.

The units of the radius parameter.

bounding_polygon_layerOptional layer (see Feature Input in documentation)

A layer specifying the polygon(s) where you want densities to be calculated.

area_unitsOptional string

The units of the calculated density values.

classification_typeOptional string

Determines how density values will be classified into polygons.

num_classesOptional int

This value is used to divide the range of predicted values into distinct classes. The range of values in each class is determined by the classificationType parameter.

output_nameOptional string

Additional properties such as output feature service name.

contextOptional string

Additional settings such as processing extent and output spatial reference.

gis :

Optional, the GIS on which this tool runs. If not specified, the active GIS is used.

result_layer : layer (FeatureCollection)

## find_hot_spots¶

analyze_patterns.find_hot_spots(analysis_field=None, divided_by_field=None, bounding_polygon_layer=None, aggregation_polygon_layer=None, output_name=None, context=None, gis=None)

The Find Hot Spots function finds statistically significant clusters of incident points, weighted points, or weighted polygons. For incident data, the analysis field (weight) is obtained by aggregation. Output is a hot spot map.

gis : The GIS used for running this analysis analysis_layer : Required layer (see Feature Input in documentation)

The point or polygon feature layer for which hot spots will be calculated.

analysis_fieldOptional string

The numeric field in the AnalysisLayer that will be analyzed.

divided_by_field : Optional string

bounding_polygon_layerOptional layer (see Feature Input in documentation)

When the analysis layer is points and no AnalysisField is specified, you can provide polygons features that define where incidents could have occurred.

aggregation_polygon_layerOptional layer (see Feature Input in documentation)

When the AnalysisLayer contains points and no AnalysisField is specified, you can provide polygon features into which the points will be aggregated and analyzed, such as administrative units.

output_nameOptional string

Additional properties such as output feature service name.

contextOptional string

Additional settings such as processing extent and output spatial reference.

gis :

Optional, the GIS on which this tool runs. If not specified, the active GIS is used.

dict with the following keys:

“hot_spots_result_layer” : layer (FeatureCollection) “process_info” : list of messages

## find_outliers¶

analyze_patterns.find_outliers(analysis_field, divided_by_field=None, bounding_polygon_layer=None, aggregation_polygon_layer=None, permutations=None, shape_type=None, cell_size=None, cell_units=None, distance_band=None, band_units=None, output_name=None, context=None, gis=None)

The Find Outliers task analyzes point data (such as crime incidents, traffic accidents, or trees) or field values associated with points or area features (such as the number of people in each census tract or the total sales for retail stores). It finds statistically significant spatial clusters of high values and low values and statistically significant high or low spatial outliers within those clusters.

The result map layer shows high outliers in red and low outliers in dark blue. Clusters of high values appear pink and clusters of low values appear light blue. Features that are beige are not a statistically significant outlier and not part of a statistically significant cluster; the spatial pattern associated with these features could very likely be the result of random processes and random chance.

analysis_layerRequired layer (see Feature Input in documentation)

The point or polygon feature layer for which outliers will be calculated.

analysis_fieldOptional string

The numeric field that will be analyzed.

divided_by_field : Optional string, The numeric field in the analysis_layer that will be used to normalize your data. bounding_polygon_layer : Optional layer (see Feature Input in documentation)

When the analysis layer is points and no analysisField is specified, you can provide polygon features that define where incidents could have occurred.

aggregation_polygon_layerOptional layer (see Feature Input in documentation)

When the AnalysisLayer contains points and no AnalysisField is specified, you can provide polygon features into which the points will be aggregated and analyzed, such as administrative units.

permutations : Permutations are used to determine how likely it would be to find the actual spatial distribution of the values you are analyzing. Choosing the number of permutations is a balance between precision and increased processing time. A lower number of permutations can be used when first exploring a problem, but it is best practice to increase the permutations to the highest number feasible for final results.

• Speed implements 199 permutations and results in p-values with a precision of 0.01.

• Balance implements 499 permutations and results in p-values with a precision of 0.002.

• Precision implements 999 permutations and results in p-values with a precision of 0.001.

Values: Speed | Balance | Precision

shape_type : optional string, The shape of the polygon mesh the input features will be aggregated into.

• Fishnet - The input features will be aggregated into a grid of square (fishnet) cells.

• Hexagon - The input features will be aggregated into a grid of hexagonal cells.

cell_size : The size of the grid cells used to aggregate your features. When aggregating into a hexagon grid, this distance is used as the height to construct the hexagon polygons. cell_units : The units of the cellSize value. You must provide a value if cellSize has been set.

Values: Miles | Feet | Kilometers | Meters

distance_band : The spatial extent of the analysis neighborhood. This value determines which features are analyzed together in order to assess local clustering. band_units : The units of the distanceBand value. You must provide a value if distanceBand has been set.

Values: Miles | Feet | Kilometers | Meters

output_nameOptional string

Additional properties such as output feature service name.

contextOptional string

Additional settings such as processing extent and output spatial reference.

gis : The GIS used for running this analysis

Item it output_name is set. dict with the following keys:

“find_outliers_result_layer” : layer (FeatureCollection) “process_info” : list of messages

## interpolate_points¶

analyze_patterns.interpolate_points(field, interpolate_option='5', output_prediction_error=False, classification_type='GeometricInterval', num_classes=10, class_breaks=[], bounding_polygon_layer=None, predict_at_point_layer=None, output_name=None, context=None, gis=None)

The Interpolate Points function allows you to predict values at new locations based on measurements from a collection of points. The function takes point data with values at each point and returns areas classified by predicted values.

input_layerRequired layer (see Feature Input in documentation)

The point layer whose features will be interpolated.

fieldRequired string

Name of the numeric field containing the values you wish to interpolate.

interpolate_optionOptional string

Integer value declaring your preference for speed versus accuracy, from 1 (fastest) to 9 (most accurate). More accurate predictions take longer to calculate.

output_prediction_errorOptional bool

If True, a polygon layer of standard errors for the interpolation predictions will be returned in the predictionError output parameter.

classification_typeOptional string

Determines how predicted values will be classified into areas.

num_classesOptional int

This value is used to divide the range of interpolated values into distinct classes. The range of values in each class is determined by the classificationType parameter. Each class defines the boundaries of the result polygons.

class_breaksOptional list of floats

If classificationType is Manual, supply desired class break values separated by spaces. These values define the upper limit of each class, so the number of classes will equal the number of entered values. Areas will not be created for any locations with predicted values above the largest entered break value. You must enter at least two values and no more than 32.

bounding_polygon_layerOptional layer (see Feature Input in documentation)

A layer specifying the polygon(s) where you want values to be interpolated.

predict_at_point_layerOptional layer (see Feature Input in documentation)

An optional layer specifying point locations to calculate prediction values. This allows you to make predictions at specific locations of interest.

output_nameOptional string

Additional properties such as output feature service name.

contextOptional string

Additional settings such as processing extent and output spatial reference.

gis :

Optional, the GIS on which this tool runs. If not specified, the active GIS is used.

dict with the following keys:

“result_layer” : layer (FeatureCollection) “prediction_error” : layer (FeatureCollection) “predicted_point_layer” : layer (FeatureCollection)